What Data for This Category Looks Like
Documents generated daily by process, equipment, and quality teams at crystalline silicon photovoltaic cell manufacturers. Data sources include silicon wafer incoming material records, MES systems, operating parameters for diffusion furnaces, coating equipment, and printing presses, EL and IV test results, and laboratory test data. Data is updated per batch of materials, per wafer test, and with daily generated reports. Document types include process cards, diffusion and coating parameter records, printing and sintering logs, EL detection images, IV grading data, and yield reports. Fields cover equipment operating parameters, test result values, and process step nodes. Data includes structured tables, semi-structured text, and image types, with no fixed unified format.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The business data of photovoltaic cell manufacturing has multiple formats and frequent updates. Before processing images, confirm support in the deployed version, edition, and vector model. Images are persisted before proceeding through the indexing lifecycle, so upload completion alone does not confirm retrievability. The continuous production workflow requires retaining contextual associations between adjacent process steps during chunking, to avoid breaking the logical chain of process parameters. Wafer-level data has numerous fine-grained fields, so the vector model must support multi-field concatenated text input. This helps associate process parameters and cost-accounting inputs when preparing tender documents and quotations.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-process parameter length of photovoltaic cell process documents, avoids breaking process logic during chunking |
| Overlap Length | 100–200 characters | Retains contextual associations of adjacent processes, prevents loss of the sequential relationship of process parameters after chunking |
defaultToken | Set the default token count for direct chunking based on samples | This field controls chunking; it is not a model ID or vector dimension. Validate it on process records |
maxToken | Set the maximum to the token limit supported by the selected vector model/service | The limit depends on the model and deployment; do not assume a fixed value |
| Similarity Threshold | Calibrated via actual testing | Matches the matching accuracy requirements of photovoltaic cell process parameters, adapts to parameter comparison needs during bidding |
| Single Search Limit | Top 5 entries | Controls the context length during tender document generation, avoids redundant data interfering with quotation calculations |
Three Common Misconfigurations
- Upload succeeds but indexing fails after an upgrade: This symptom alone does not show that the
defaultTokenrange changed. Check the full task logs, effective chunking settings, input field lengths, and the selected model/service limits. - Indexing remains incomplete for an extended period: Use the logs to identify the processing stage, then check file format, model limits, service health, and available resources. Do not assume the deployment has a breakpoint-resume indexing switch.
- Abnormal reference results after switching vector models: The knowledge base reference returns empty results, and the frontend displays no matching content. This occurs because the vector model dimension configuration is not updated synchronously, so the original indexing vectors do not match the vector space of the new model.
How to Verify Proper Configuration
- Upload a single standard process card document, access the chunking preview interface, and confirm that the text length after chunking falls within the configured chunk length range.
- If these fields are set in a custom vector-model configuration, confirm that
defaultTokenis the direct-chunking token count andmaxTokendoes not exceed the selected model/service limit; validate both on process samples. - Upload an EL detection image supported by the deployment, wait for the knowledge-base training and indexing lifecycle to complete, then search for related image content. If the deployment or model does not support that input type, use a supported sample.
The values provided are common starting points and should be evaluated against the reader’s own samples.